mlsanityData to 14 Oct 2026

Towards Type Agnostic Cyber Defense Agents

arXiv 2412.01542 ↗First posted 2 Dec 2024cs.CR cs.AI cs.GTcited by 0

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

With computing now ubiquitous across government, industry, and education, cybersecurity has become a critical component for every organization on the planet. Due to this ubiquity of computing, cyber threats have continued to grow year over year, leading to labor shortages and a skills gap in cybersecurity. As a result, many cybersecurity product vendors and security organizations have looked to artificial intelligence to shore up their defenses. This work considers how to characterize attackers and defenders in one approach to the automation of cyber defense -- the application of reinforcement learning. Specifically, we characterize the types of attackers and defenders in the sense of Bayesian games and, using reinforcement learning, derive empirical findings about how to best train agents that defend against multiple types of attackers.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 28 references that are in the map. First posted on arXiv 2 Dec 2024.

Builds on

9 of 28 references.

References · 9

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,558
  3. 2021Deep Reinforcement Learning at the Edge of the Statistical Precipicecited by 807
  4. 2019Deep Reinforcement Learning for Cyber Securitycited by 95
  5. 2019Sub-policy Adaptation for Hierarchical Reinforcement Learningcited by 47
  6. 2015A Game-Theoretic Model and Best-Response Learning Method for Ad Hoc Coordination in Multiagent Systemscited by 44
  7. 2022Developing Optimal Causal Cyber-Defence Agents via Cyber Security Simulationcited by 37
  8. 2022Simulation of Attacker Defender Interaction in a Noisy Security Gamecited by 2
  9. 2024The Price of Pessimism for Automated Defensecited by 1

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  2. 2025Automated Cyber Defense with Generalizable Graph-based Reinforcement Learning Agents
  3. 2024Hierarchical Multi-agent Reinforcement Learning for Cyber Network Defense
  4. 2025An Empirical Game-Theoretic Analysis of Autonomous Cyber-Defence Agents
  5. 2026Distilling Knowledge from Large Language Models into Lightweight Reinforcement Learning Agents for Autonomous Cyber Operations
  6. 2024Autonomous Network Defence using Reinforcement Learning
  7. 2025Quantitative Resilience Modeling for Autonomous Cyber Defense
  8. 2025Cybersecurity AI: Evaluating Agentic Cybersecurity in Attack/Defense CTFs